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1 Data Summary

2 BridgeSettings

Configuration Settings for the Workflow

species tissue metadata genesets genetype min_umi_per_cell max_mt_percent min_genes_per_cell min_cell scr_th seu_nrmlz_method seu_scale_factor seu_n_hvg seu_n_dim seu_k_param seu_cluster_res harmony tsne spr_n_dim mrk_logfc mrk_min_pct mrk_only_pos mrk_test mrk_top_n adt trajectory traj_var_gene traj_top_n pipe_version
hs lung sample_based none none 750 20 250 3 0.25 LogNormalize 1e+06 2000 30 20 0.7 sample FALSE 30 0.25 0.5 TRUE wilcox 0 FALSE none 1000 50 1.0.0

3 BridgeQC

3.1 Sample-level Characteristics and Quality Control Metrics

3.1.1 Metadata

sample disease tissue sample_id
THD0001 Control Lung S1
THD0002 Control Lung S2
THD0005 Control Lung S3
TILD001 IPF Lung S4
TILD006 IPF Lung S5
TILD010 IPF Lung S6
TILD015 IPF Lung S7
TILD019 Unclassifiable ILD Lung S8
TILD028 IPF Lung S9
TILD030 sacroidosis Lung S10
VUHD65 Control Lung S11
VUHD66 Control Lung S12
VUHD67 Control Lung S13
VUHD68 Control Lung S14
VUHD69 Control Lung S15
VUHD70 Control Lung S16
VUHD71 Control Lung S17
VUILD48 NSIP Lung S18
VUILD53 IPF Lung S19
VUILD54 cHP Lung S20
VUILD55 NSIP Lung S21
VUILD57 sacroidosis Lung S22
VUILD58 cHP Lung S23
VUILD59 IPF Lung S24
VUILD60 IPF Lung S25
VUILD61 IPF Lung S26
VUILD62 NSIP Lung S27
VUILD63 IPF Lung S28
VUILD64 IPF Lung S29
VUILD65 IPF Lung S30

3.1.2 Cells & Genes (RNA)

sample_id pre_qc_gene pre_qc_cell post_qc_gene post_qc_cell
S1 21,852 2,218 18,481 2,154
S2 22,384 2,647 19,145 2,552
S3 23,043 3,494 19,807 3,424
S4 21,888 2,128 18,590 2,070
S5 22,653 3,313 19,452 3,235
S6 21,044 1,584 17,691 1,523
S7 23,793 6,402 20,699 6,203
S8 23,592 4,006 20,437 3,905
S9 23,641 4,393 20,466 4,300
S10 22,754 3,082 19,607 2,978
S11 22,663 2,552 19,511 2,506
S12 22,778 2,802 19,492 2,757
S13 24,705 7,612 21,701 7,437
S14 24,253 5,815 21,219 5,454
S15 19,231 800 16,093 783
S16 21,476 1,509 18,244 1,488
S17 22,368 2,195 19,246 2,100
S18 17,992 376 14,632 357
S19 21,192 1,116 17,901 1,094
S20 19,422 750 16,151 722
S21 22,477 2,749 19,272 2,692
S22 21,746 1,809 18,510 1,775
S23 24,949 6,785 21,835 6,514
S24 26,389 15,142 23,547 14,747
S25 24,381 8,463 21,173 8,325
S26 24,601 8,408 21,633 8,263
S27 23,839 5,513 20,769 5,391
S28 21,557 2,124 18,339 2,073
S29 21,865 2,001 18,571 1,965
S30 22,653 2,608 19,344 2,560

3.1.3 UMI per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 1,354 1,354 3,340 5,170 9,001 53,017 53,017
S2 1,481 1,481 3,942 7,153 12,240 58,298 58,298
S3 1,396 1,396 3,472 5,418 9,571 64,081 64,081
S4 1,499 1,499 3,450 5,543 10,149 53,439 53,439
S5 1,430 1,430 3,889 6,932 12,116 64,693 64,693
S6 1,382 1,382 5,182 9,412 14,681 44,794 44,794
S7 1,377 1,377 4,034 7,240 12,720 50,216 50,216
S8 1,458 1,458 3,521 5,726 9,786 55,047 55,047
S9 1,552 1,552 3,570 5,502 9,593 63,352 63,352
S10 1,408 1,408 3,278 4,991 8,521 52,055 52,055
S11 1,525 1,525 3,496 5,783 10,425 52,099 52,099
S12 1,372 1,372 3,211 4,932 8,522 51,620 51,620
S13 1,377 1,377 3,433 5,669 9,724 69,750 69,750
S14 1,384 1,384 4,740 7,250 11,564 69,488 69,488
S15 1,576 1,576 3,659 5,992 10,455 34,120 34,120
S16 1,600 1,600 3,874 6,428 13,029 69,474 69,474
S17 1,564 1,564 3,806 6,366 10,873 54,634 54,634
S18 1,550 1,550 5,000 8,592 13,619 42,669 42,669
S19 1,596 1,596 3,734 6,432 10,925 59,936 59,936
S20 1,471 1,471 4,149 7,665 12,405 41,821 41,821
S21 1,447 1,447 3,553 5,956 10,747 42,813 42,813
S22 1,591 1,591 3,764 6,731 12,734 57,948 57,948
S23 1,465 1,465 4,261 7,828 12,281 75,020 75,020
S24 1,351 1,351 4,390 8,089 13,216 77,990 77,990
S25 1,393 1,393 3,982 6,231 10,136 85,457 85,457
S26 1,429 1,429 3,692 6,121 10,416 67,350 67,350
S27 1,554 1,554 3,694 7,138 14,108 87,520 87,520
S28 1,442 1,442 3,676 6,766 12,019 51,119 51,119
S29 1,417 1,417 3,102 4,903 8,325 67,572 67,572
S30 1,453 1,453 3,556 5,910 10,550 41,210 41,210

3.1.4 Gene per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 1,000 1,000 1,383 1,866 2,584 6,911 6,911
S2 1,000 1,000 1,531 2,248 2,959 7,381 7,381
S3 998 998 1,364 1,888 2,676 7,087 7,087
S4 1,000 1,000 1,358 1,846 2,671 7,324 7,324
S5 1,000 1,000 1,505 2,162 2,898 7,219 7,219
S6 998 998 1,762 2,488 3,158 6,770 6,770
S7 1,000 1,000 1,488 2,160 2,910 6,917 6,917
S8 999 999 1,449 2,009 2,748 7,258 7,258
S9 1,001 1,001 1,408 1,951 2,716 6,423 6,423
S10 999 999 1,410 1,881 2,584 6,899 6,899
S11 1,002 1,002 1,455 2,026 2,961 7,732 7,732
S12 1,002 1,002 1,381 1,863 2,618 7,459 7,459
S13 1,000 1,000 1,401 1,990 2,835 7,717 7,717
S14 1,000 1,000 1,753 2,528 3,364 7,517 7,517
S15 1,002 1,002 1,380 1,936 2,618 5,027 5,027
S16 997 997 1,442 2,082 3,116 8,282 8,282
S17 999 999 1,460 2,122 3,319 7,929 7,929
S18 991 991 1,664 2,383 2,966 6,733 6,733
S19 1,000 1,000 1,503 2,169 3,115 8,280 8,280
S20 990 990 1,534 2,233 2,887 5,665 5,665
S21 998 998 1,387 1,978 2,838 6,753 6,753
S22 1,002 1,002 1,463 2,150 3,034 6,740 6,740
S23 1,000 1,000 1,627 2,496 3,417 7,752 7,752
S24 999 999 1,639 2,491 3,390 8,605 8,605
S25 1,000 1,000 1,316 1,733 2,589 6,749 6,749
S26 1,000 1,000 1,530 2,093 2,860 7,445 7,445
S27 1,000 1,000 1,422 2,192 3,250 7,802 7,802
S28 1,000 1,000 1,463 2,106 2,904 6,195 6,195
S29 999 999 1,420 1,905 2,602 6,896 6,896
S30 997 997 1,505 2,106 2,952 6,573 6,573

3.1.5 Mitochondrial (%) per cell (RNA)

Vales are post-QC.

sample_id min 0% 25% 50% 75% 100% max
S1 0.00 0.00 3.65 5.03 7.21 19.94 19.94
S2 0.00 0.00 3.82 5.30 7.69 19.91 19.91
S3 0.00 0.00 3.58 5.13 7.39 19.84 19.84
S4 0.00 0.00 4.45 6.61 9.39 19.85 19.85
S5 0.00 0.00 4.24 6.01 8.31 19.91 19.91
S6 0.00 0.00 3.77 5.31 7.26 19.93 19.93
S7 0.00 0.00 4.44 6.02 8.09 19.91 19.91
S8 0.00 0.00 3.47 5.21 7.94 19.97 19.97
S9 0.00 0.00 3.71 5.39 7.92 20.00 20.00
S10 0.00 0.00 3.50 5.35 8.32 19.96 19.96
S11 0.00 0.00 3.53 5.06 7.72 19.82 19.82
S12 0.00 0.00 3.41 4.99 7.33 19.96 19.96
S13 0.00 0.00 4.21 6.19 9.11 19.98 19.98
S14 0.00 0.00 5.22 8.30 11.77 19.95 19.95
S15 0.02 0.02 4.29 5.94 8.17 19.72 19.72
S16 0.00 0.00 2.77 4.04 5.96 19.72 19.72
S17 0.00 0.00 4.26 6.50 9.56 19.92 19.92
S18 0.00 0.00 4.82 6.44 8.51 18.87 18.87
S19 0.00 0.00 3.17 4.74 7.60 19.93 19.93
S20 0.02 0.02 3.86 5.47 7.80 19.99 19.99
S21 0.00 0.00 3.99 5.58 8.00 19.99 19.99
S22 0.00 0.00 3.65 5.19 7.25 19.88 19.88
S23 0.00 0.00 4.32 6.82 10.09 19.99 19.99
S24 0.00 0.00 3.71 5.42 8.46 19.99 19.99
S25 0.00 0.00 3.42 4.44 6.02 20.00 20.00
S26 0.00 0.00 3.02 4.30 6.37 19.96 19.96
S27 0.00 0.00 4.10 5.64 7.67 19.99 19.99
S28 0.00 0.00 4.29 5.96 8.08 19.90 19.90
S29 0.00 0.00 3.01 4.36 6.62 19.71 19.71
S30 0.00 0.00 3.16 4.48 7.24 19.89 19.89

3.2 Distribution Plots for Quality Control Metrics

Thresholds, represented by dashed lines, were implemented to filter the data and only retain cells of high quality.

3.2.1 UMI per cell

3.2.2 Gene per cell

3.2.3 Mitochondrial (%) per cell

3.3 Barcodes contamination

3.3.1 Raw count

Number of barcodes shared between pairs of samples post-QC.

## [1] "No overlaps among samples' barcodes."

3.3.2 Jaccard Index

Fraction (%) of barcodes shared between pairs of samples post-QC.

## [1] "No overlaps among samples' barcodes."

3.4 Doublet Cell Detection using Scrublet Scoring System

Observed scores are used for doublet classification. Dashed line indicates the threshold used to identify doublets.

3.4.1 Observed scores

3.4.2 Simulated scores

3.5 Impact of Quality Control on Cell, Gene, and UMI Abundances

3.5.1 Number of cells and genes per sample

diamonds and diamonds refer to before and after QC, respectively.

3.5.2 Average number of UMIs/cell and genes/cell per sample

The error bars represent the standard deviation of the number of UMIs and genes across cells per sample.

3.6 Key QC Metrics of Merged Samples

min 0% 25% 50% 75% 100% max
UMI per cell 1,351 1,351 3,789 6,475 11,296 87,520 87,520
Gene per cell 990 990 1,467 2,114 2,991 8,605 8,605
Mitochondrial (%) per cell 0.00 0.00 3.73 5.43 8.14 20.00 20.00

3.7 Visualization of Merged scRNA-Seq Object in 2D Space

Note: Labels may have been removed if they overlap excessively.

3.7.1 Signacx SPRING Projection

3.7.2 Seurat UMAP Projection

3.7.3 Seurat UMAP Projection (no-batch correction)

3.8 Visualizing Cell QC Metrics Using Dimensionality Reduction

Note: QC metrics based on kernel density estimation.

3.8.1 UMI per cell - SPRING Projection

3.8.2 Gene per cell - SPRING Projection

3.8.3 Mitochondrial (%) per cell - SPRING Projection

3.8.4 UMI per cell - UMAP Projection

3.8.5 Gene per cell - UMAP Projection

3.8.6 Mitochondrial (%) per cell - UMAP Projection

4 BridgeCluster

Note: Labels may have been removed if they overlap excessively.

4.1 Visualizing Cell Populations with Dimensionality Reduction

4.1.1 Signacx SPRING Projection

4.1.2 Seurat UMAP Projection

4.2 Cell Cluster Composition

Values at the top of each bar indicate the percentage of cells

4.2.1 Seurat

4.2.2 Signacx

4.3 Statistical Dispersion Analysis of Cell Composition in Clusters across sample

This measurement is a proxy to batch effect artifacts. Values adjacent to each point indicate the number of cells.

4.3.1 Seurat

4.3.2 Signacx

5 BridgeAnnotation

5.1 Visualizing Distinct Cell Type Populations with Dimensionality Reduction

Note: Labels may have been removed if they overlap excessively.

5.1.1 Signacx SPRING Projection

5.1.2 Signacx UMAP Projection

5.2 Cell Type Population Composition

Values at the top of each bar indicate the percentage of cells

5.2.1 Signacx

6 References

Publication: no referrence

Data Availability: no referrence

7 Session Information

This is the output of sessionInfo() on the computing system on which this document was compiled

## R version 4.3.2 (2023-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## Random number generation:
##  RNG:     L'Ecuyer-CMRG 
##  Normal:  Inversion 
##  Sample:  Rejection 
##  
## locale:
## [1] C
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    grid      stats     graphics  grDevices
## [6] utils     datasets  methods   base     
## 
## other attached packages:
##  [1] edgeR_4.0.2                 limma_3.58.1               
##  [3] slingshot_2.10.0            TrajectoryUtils_1.10.0     
##  [5] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
##  [7] Biobase_2.62.0              GenomicRanges_1.54.1       
##  [9] GenomeInfoDb_1.38.1         IRanges_2.36.0             
## [11] S4Vectors_0.40.2            BiocGenerics_0.48.1        
## [13] MatrixGenerics_1.14.0       matrixStats_1.1.0          
## [15] princurve_2.1.6             Nebulosa_1.12.0            
## [17] patchwork_1.1.3             data.table_1.14.10         
## [19] ComplexHeatmap_2.18.0       visNetwork_2.1.2           
## [21] plotly_4.10.3               data.tree_1.1.0            
## [23] DT_0.30                     readxl_1.4.3               
## [25] gtools_3.9.5                gplots_3.1.3               
## [27] gridtext_0.1.5              igraph_1.5.1               
## [29] sargent_1.0.1               SignacX_2.2.5              
## [31] harmony_1.2.0               Rcpp_1.0.11                
## [33] kableExtra_1.3.4            purrr_1.0.2                
## [35] reticulate_1.34.0           RColorBrewer_1.1-3         
## [37] cowplot_1.1.1               gridExtra_2.3              
## [39] pheatmap_1.0.12             ggrepel_0.9.4              
## [41] ggplot2_3.4.4               Seurat_5.0.1               
## [43] SeuratObject_5.0.1          sp_2.1-2                   
## [45] dplyr_1.1.4                 optparse_1.7.3             
## 
## loaded via a namespace (and not attached):
##   [1] spatstat.sparse_3.0-3   bitops_1.0-7           
##   [3] httr_1.4.7              webshot_0.5.5          
##   [5] doParallel_1.0.17       tools_4.3.2            
##   [7] sctransform_0.4.1       utf8_1.2.4             
##   [9] R6_2.5.1                mgcv_1.9-0             
##  [11] lazyeval_0.2.2          uwot_0.1.16            
##  [13] ggdist_3.3.1            GetoptLong_1.0.5       
##  [15] withr_2.5.2             progressr_0.14.0       
##  [17] cli_3.6.1               spatstat.explore_3.2-5 
##  [19] fastDummies_1.7.3       labeling_0.4.3         
##  [21] sass_0.4.8              mvtnorm_1.2-4          
##  [23] spatstat.data_3.0-3     proxy_0.4-27           
##  [25] ggridges_0.5.4          pbapply_1.7-2          
##  [27] commonmark_1.9.0        systemfonts_1.0.5      
##  [29] R.utils_2.12.3          svglite_2.1.2          
##  [31] parallelly_1.36.0       rstudioapi_0.15.0      
##  [33] generics_0.1.3          shape_1.4.6            
##  [35] crosstalk_1.2.1         ica_1.0-3              
##  [37] spatstat.random_3.2-2   distributional_0.3.2   
##  [39] Matrix_1.6-4            fansi_1.0.6            
##  [41] DescTools_0.99.52       abind_1.4-5            
##  [43] R.methodsS3_1.8.2       lifecycle_1.0.4        
##  [45] yaml_2.3.7              SparseArray_1.2.2      
##  [47] Rtsne_0.17              promises_1.2.1         
##  [49] crayon_1.5.2            miniUI_0.1.1.1         
##  [51] lattice_0.22-5          pillar_1.9.0           
##  [53] knitr_1.45              rjson_0.2.21           
##  [55] boot_1.3-28.1           gld_2.6.6              
##  [57] future.apply_1.11.0     codetools_0.2-19       
##  [59] leiden_0.4.3.1          glue_1.6.2             
##  [61] vctrs_0.6.5             png_0.1-8              
##  [63] spam_2.10-0             neuralnet_1.44.2       
##  [65] cellranger_1.1.0        gtable_0.3.4           
##  [67] cachem_1.0.8            ks_1.14.1              
##  [69] xfun_0.41               S4Arrays_1.2.0         
##  [71] mime_0.12               pracma_2.4.4           
##  [73] survival_3.5-7          pbmcapply_1.5.1        
##  [75] iterators_1.0.14        statmod_1.5.0          
##  [77] ellipsis_0.3.2          fitdistrplus_1.1-11    
##  [79] ROCR_1.0-11             nlme_3.1-164           
##  [81] RcppAnnoy_0.0.21        bslib_0.6.1            
##  [83] irlba_2.3.5.1           KernSmooth_2.23-22     
##  [85] colorspace_2.1-0        Exact_3.2              
##  [87] tidyselect_1.2.0        compiler_4.3.2         
##  [89] rvest_1.0.3             expm_0.999-8           
##  [91] xml2_1.3.6              DelayedArray_0.28.0    
##  [93] scales_1.3.0            caTools_1.18.2         
##  [95] lmtest_0.9-40           stringr_1.5.1          
##  [97] digest_0.6.33           goftest_1.2-3          
##  [99] spatstat.utils_3.0-4    rmarkdown_2.25         
## [101] RhpcBLASctl_0.23-42     XVector_0.42.0         
## [103] htmltools_0.5.7         pkgconfig_2.0.3        
## [105] highr_0.10              fastmap_1.1.1          
## [107] rlang_1.1.2             GlobalOptions_0.1.2    
## [109] htmlwidgets_1.6.4       shiny_1.8.0            
## [111] jquerylib_0.1.4         farver_2.1.1           
## [113] zoo_1.8-12              jsonlite_1.8.8         
## [115] mclust_6.0.1            R.oo_1.25.0            
## [117] RCurl_1.98-1.13         magrittr_2.0.3         
## [119] GenomeInfoDbData_1.2.11 dotCall64_1.1-1        
## [121] munsell_0.5.0           stringi_1.8.2          
## [123] rootSolve_1.8.2.4       zlibbioc_1.48.0        
## [125] MASS_7.3-60             plyr_1.8.9             
## [127] parallel_4.3.2          listenv_0.9.0          
## [129] lmom_3.0                deldir_2.0-2           
## [131] splines_4.3.2           tensor_1.5             
## [133] circlize_0.4.15         locfit_1.5-9.8         
## [135] spatstat.geom_3.2-7     markdown_1.12          
## [137] RcppHNSW_0.5.0          reshape2_1.4.4         
## [139] evaluate_0.23           foreach_1.5.2          
## [141] httpuv_1.6.13           RANN_2.6.1             
## [143] tidyr_1.3.0             getopt_1.20.4          
## [145] polyclip_1.10-6         future_1.33.0          
## [147] clue_0.3-65             scattermore_1.2        
## [149] xtable_1.8-4            e1071_1.7-14           
## [151] RSpectra_0.16-1         later_1.3.2            
## [153] viridisLite_0.4.2       class_7.3-22           
## [155] tibble_3.2.1            cluster_2.1.6          
## [157] globals_0.16.2